class_docstr: A trainer for training models. 
class_docstr_tok: ["A","trainer","for","training","models","."]
code: def predict(self, batch, unsort=True):
      device = next(self.model.parameters()).device
      (
          inputs,
          orig_idx,
          word_orig_idx,
          sentlens,
          wordlens,
          text,
      ) = unpack_batch(batch, device)
      (
          word,
          word_mask,
          wordchars,
          wordchars_mask,
          upos,
          xpos,
          ufeats,
          pretrained,
          lemma,
          head,
          deprel,
      ) = inputs
  
      self.model.eval()
      batch_size = word.size(0)
      _, preds = self.model(
          word,
          word_mask,
          wordchars,
          wordchars_mask,
          upos,
          xpos,
          ufeats,
          pretrained,
          lemma,
          head,
          deprel,
          word_orig_idx,
          sentlens,
          wordlens,
          text,
      )
      head_seqs = [
          chuliu_edmonds_one_root(adj[:l, :l])[1:]
          for adj, l in zip(preds[0], sentlens)
      ]  # remove attachment for the root
      deprel_seqs = [
          self.vocab["deprel"].unmap(
              [preds[1][i][j + 1][h] for j, h in enumerate(hs)]
          )
          for i, hs in enumerate(head_seqs)
      ]
  
      pred_tokens = [
          [
              [str(head_seqs[i][j]), deprel_seqs[i][j]]
              for j in range(sentlens[i] - 1)
          ]
          for i in range(batch_size)
      ]
      if unsort:
          pred_tokens = utils.unsort(pred_tokens, orig_idx)
      return pred_tokens
  
file_path: stanza/stanza/models/depparse/trainer.py
imports: import sys
  import logging
  import torch
  from torch import nn
  from stanza.models.common.trainer import Trainer
  from stanza.models.common import utils, loss
  from stanza.models.common.foundation_cache import NoTransformerFoundationCache
  from stanza.models.common.chuliu_edmonds import chuliu_edmonds_one_root
  from stanza.models.depparse.model import Parser
  from stanza.models.pos.vocab import MultiVocab
label_desc: Use image node_id 3 for calling the Trainer obj's underlying member method code with example usage: obj.predict(batch, unsort) and returns: pred_tokens